Typical methods for non-contact capture of three-dimensional (3D) structure principally include conventional (dual-imager) stereo vision, laser ranging (e.g., scanning and flash light detection and ranging (LIDAR)), and structured light methods.
Conventional stereo vision is a well-established ranging method that typically estimates range to a scene through measurements of pixel offsets between images of the same object taken simultaneously from two spatially separated cameras. The principal disadvantages of conventional stereo are the need for a second imager and the increased size and power requirements. In particular, it is generally impractical to convert the existing installed base of single-camera devices, such as smartphones or tablets, into stereo cameras due to the challenges associated with integrating a second camera.
Laser ranging methods, such as LIDAR, typically employ the time-of-flight principle to determine range to objects. For example, the LIDAR sensor emits light pulses, receives reflected energy from the object of interest, and calculates distance through precise time of flight measurements. As active illumination sensors, LIDARs are relatively large (typically greater than 2 cu-in), power-intensive (typically greater than 10 W), and expensive. More distant objects require higher illumination power, thus further driving up the size and power consumption of the sensor. They are also not easily integrated into existing/ubiquitous platforms such as smartphones and tablets.
Structured light techniques typically employ a single camera together with a physically separated laser/illuminator that projects a pattern on to the object to be measured. The position and shape of the pattern indicates surface profile shape and location. As with LIDAR, collecting 3D images, particularly at longer ranges and outdoors, requires considerable laser power, thus rendering this approach impractical.
As such, a need exists for improved systems and methods for converting conventional imaging devices into 3D cameras.